The development and use of deep RL systems has raised awareness of two serious drawbacks of representations that are shaped by RL alone. One problem is that task-linked rewards are generally sparse. In chess, for example, reward occurs once per game, making it a weak signal for learning about opening moves. A second problem is over-fitting – internal representations shaped exclusively by task-specific rewards may end up being useful only for tasks the learner has performed, but completely wrong for new tasks [1]}, [2]}. Better would be some learning procedure that gives rise to internal representations that are more broadly useful, supporting transfer between tasks.
